/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    RuleStats.java
 *    Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 */

package weka.classifiers.rules;

import java.io.Serializable;
import java.util.Enumeration;
import java.util.Random;

import weka.core.Attribute;
import weka.core.FastVector;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

/**
 * This class implements the statistics functions used in the 
 * propositional rule learner, from the simpler ones like count of
 * true/false positive/negatives, filter data based on the ruleset, etc.
 * to the more sophisticated ones such as MDL calculation and rule
 * variants generation for each rule in the ruleset. <p>
 *
 * Obviously the statistics functions listed above need the specific
 * data and the specific ruleset, which are given in order to instantiate
 * an object of this class. <p>
 *  
 * @author Xin Xu (xx5@cs.waikato.ac.nz)
 * @version $Revision: 8034 $
 */
public class RuleStats 
  implements Serializable, RevisionHandler {
  
  /** for serialization */
  static final long serialVersionUID = -5708153367675298624L;

  /** The data on which the stats calculation is based */
  private Instances m_Data;

  /** The specific ruleset in question */
  private FastVector m_Ruleset;

  /** The simple stats of each rule */
  private FastVector m_SimpleStats;

  /** The set of instances filtered by the ruleset */
  private FastVector m_Filtered;
    
  /** The total number of possible conditions that could
   *  appear in a rule */
  private double m_Total;
 
  /** The redundancy factor in theory description length */	
  private static double REDUNDANCY_FACTOR = 0.5;
    
  /** The theory weight in the MDL calculation */
  private double MDL_THEORY_WEIGHT = 1.0;

    /** The class distributions predicted by each rule */
    private FastVector m_Distributions;

  /** Default constructor */
  public RuleStats(){
    m_Data = null;
    m_Ruleset = null;
    m_SimpleStats = null;
    m_Filtered = null;
    m_Distributions = null;
    m_Total = -1;
  }

    
  /** 
   * Constructor that provides ruleset and data 
   *
   * @param data the data
   * @param rules the ruleset
   */
  public RuleStats(Instances data, FastVector rules){
    this();
    m_Data = data;
    m_Ruleset = rules;	
  }

  /**
   * Frees up memory after classifier has been built.
   */
  public void cleanUp() {
    m_Data     = null;
    m_Filtered = null;
  }

  /**
   * Set the number of all conditions that could appear 
   * in a rule in this RuleStats object, if the number set
   * is smaller than 0 (typically -1), then it calcualtes
   * based on the data store 
   *
   * @param total the set number
   */
  public void setNumAllConds(double total){
    if(total < 0)
      m_Total = numAllConditions(m_Data);    
    else
      m_Total = total;
  }
    
  /** 
   * Set the data of the stats, overwriting the old one if any 
   *
   * @param data the data to be set
   */
  public void setData(Instances data){
    m_Data = data;
  }
    
  /** 
   * Get the data of the stats 
   *
   * @return the data 
   */
  public Instances getData(){
    return m_Data;
  }

    
  /** 
   * Set the ruleset of the stats, overwriting the old one if any 
   *      
   * @param rules the set of rules to be set
   */
  public void setRuleset(FastVector rules){
    m_Ruleset = rules;
  }

    
  /** 
   * Get the ruleset of the stats 
   *      
   * @return the set of rules 
   */
  public FastVector getRuleset(){
    return m_Ruleset;
  }

  /** 
   * Get the size of the ruleset in the stats 
   *      
   * @return the size of ruleset 
   */
  public int getRulesetSize(){
    return m_Ruleset.size();
  }
  
  /** 
   * Get the simple stats of one rule, including 6 parameters:
   * 0: coverage; 1:uncoverage; 2: true positive; 3: true negatives;
   * 4: false positives; 5: false negatives
   *
   * @param index the index of the rule
   * @return the stats
   */
  public double[] getSimpleStats(int index){
    if((m_SimpleStats != null) && (index < m_SimpleStats.size()))
      return (double[])m_SimpleStats.elementAt(index);
	
    return null;
  }    


  /**
   * Get the data after filtering the given rule
   *
   * @param index the index of the rule
   * @return the data covered and uncovered by the rule
   */
  public Instances[] getFiltered(int index){
	
    if((m_Filtered != null) && (index < m_Filtered.size()))
      return (Instances[])m_Filtered.elementAt(index);
	
    return null;
  }    

    /**
     * Get the class distribution predicted by the rule in
     * given position
     *
     * @param index the position index of the rule
     * @return the class distributions
     */
    public double[] getDistributions(int index){
	
	if((m_Distributions != null) && (index < m_Distributions.size()))
	    return (double[])m_Distributions.elementAt(index);
	
	return null;
    }    
    
  /**
   * Set the weight of theory in MDL calcualtion
   *
   * @param weight the weight to be set
   */
  public void setMDLTheoryWeight(double weight){
    MDL_THEORY_WEIGHT = weight;
  } 

  /**
   * Compute the number of all possible conditions that could 
   * appear in a rule of a given data.  For nominal attributes,
   * it's the number of values that could appear; for numeric 
   * attributes, it's the number of values * 2, i.e. <= and >=
   * are counted as different possible conditions.
   *
   * @param data the given data
   * @return number of all conditions of the data
   */
  public static double numAllConditions(Instances data){
    double total = 0;
    Enumeration attEnum = data.enumerateAttributes();	
    while(attEnum.hasMoreElements()){
      Attribute att= (Attribute)attEnum.nextElement();
      if(att.isNominal())
	total += (double)att.numValues();
      else
	total += 2.0 * (double)data.numDistinctValues(att);	
    }
    return total;
  }


  /**
   * Filter the data according to the ruleset and compute the basic
   * stats: coverage/uncoverage, true/false positive/negatives of 
   * each rule
   */
  public void countData(){
    if((m_Filtered != null) ||
       (m_Ruleset == null)  ||
       (m_Data == null))
      return;
	
    int size = m_Ruleset.size();
    m_Filtered = new FastVector(size);
    m_SimpleStats = new FastVector(size);
    m_Distributions = new FastVector(size);
    Instances data = new Instances(m_Data);
	
    for(int i=0; i < size; i++){
      double[] stats = new double[6];  // 6 statistics parameters
      double[] classCounts = new double[m_Data.classAttribute().numValues()];
      Instances[] filtered = computeSimpleStats(i, data, stats, classCounts);
      m_Filtered.addElement(filtered);
      m_SimpleStats.addElement(stats);
      m_Distributions.addElement(classCounts);
      data = filtered[1];  // Data not covered
    }	
  }
    
    /**
     * Count data from the position index in the ruleset
     * assuming that given data are not covered by the rules
     * in position 0...(index-1), and the statistics of these
     * rules are provided.<br>
     * This procedure is typically useful when a temporary 
     * object of RuleStats is constructed in order to efficiently
     * calculate the relative DL of rule in position index, 
     * thus all other stuff is not needed.
     *
     * @param index the given position
     * @param uncovered the data not covered by rules before index
     * @param prevRuleStats the provided stats of previous rules
     */
    public void countData(int index, Instances uncovered, 
			  double[][] prevRuleStats){
	if((m_Filtered != null) ||
	   (m_Ruleset == null))
	    return;
	
	int size = m_Ruleset.size();
	m_Filtered = new FastVector(size);
	m_SimpleStats = new FastVector(size);
	Instances[] data = new Instances[2];
	data[1] = uncovered;
	
	for(int i=0; i < index; i++){
	    m_SimpleStats.addElement(prevRuleStats[i]);
	    if(i+1 == index)
		m_Filtered.addElement(data);
	    else
		m_Filtered.addElement(new Object()); // Stuff sth.
	}
	
	for(int j=index; j < size; j++){
	    double[] stats = new double[6];  // 6 statistics parameters
	    Instances[] filtered = computeSimpleStats(j, data[1], stats, null);
	    m_Filtered.addElement(filtered);
	    m_SimpleStats.addElement(stats);
	    data = filtered;  // Data not covered
	}	
    }
    
  /**
   * Find all the instances in the dataset covered/not covered by 
   * the rule in given index, and the correponding simple statistics
   * and predicted class distributions are stored in the given double array,
   * which can be obtained by getSimpleStats() and getDistributions().<br>
   * 
   * @param index the given index, assuming correct
   * @param insts the dataset to be covered by the rule
   * @param stats the given double array to hold stats, side-effected
   * @param dist the given array to hold class distributions, side-effected
   *             if null, the distribution is not necessary 
   * @return the instances covered and not covered by the rule
   */
  private Instances[] computeSimpleStats(int index, Instances insts, 
					 double[] stats, double[] dist){
    Rule rule = (Rule)m_Ruleset.elementAt(index);
	
    Instances[] data = new Instances[2];
    data[0] = new Instances(insts, insts.numInstances());
    data[1] = new Instances(insts, insts.numInstances());

    for(int i=0; i<insts.numInstances(); i++){
      Instance datum = insts.instance(i);
      double weight = datum.weight();
      if(rule.covers(datum)){
	data[0].add(datum);        // Covered by this rule
	stats[0] += weight;        // Coverage
	if((int)datum.classValue() == (int)rule.getConsequent())
	  stats[2] += weight;    // True positives
	else
	  stats[4] += weight;    // False positives
	if(dist != null)
	    dist[(int)datum.classValue()] += weight;
      }
      else{
	data[1].add(datum);        // Not covered by this rule
	stats[1] += weight; 
	if((int)datum.classValue() != (int)rule.getConsequent())
	  stats[3] += weight;    // True negatives
	else
	  stats[5] += weight;    // False negatives	    
      }
    }
    
    return data;
  }
    
    
  /** 
   * Add a rule to the ruleset and update the stats
   *
   * @param lastRule the rule to be added
   */
  public void addAndUpdate(Rule lastRule){
    if(m_Ruleset == null)
      m_Ruleset = new FastVector();
    m_Ruleset.addElement(lastRule);
  
    Instances data = (m_Filtered == null) ?
      m_Data : ((Instances[])m_Filtered.lastElement())[1];
    double[] stats = new double[6];
    double[] classCounts = new double[m_Data.classAttribute().numValues()];
    Instances[] filtered = 
	computeSimpleStats(m_Ruleset.size()-1, data, stats, classCounts);
    
    if(m_Filtered == null)
	m_Filtered = new FastVector();	
    m_Filtered.addElement(filtered);

    if(m_SimpleStats == null)
      m_SimpleStats = new FastVector();	
    m_SimpleStats.addElement(stats);

    if(m_Distributions == null)
	m_Distributions = new FastVector();
    m_Distributions.addElement(classCounts);
  }
    
    
  /**
   * Subset description length: <br>
   * S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)
   *
   * Details see Quilan: "MDL and categorical theories (Continued)",ML95
   *
   * @param t the number of elements in a known set
   * @param k the number of elements in a subset
   * @param p the expected proportion of subset known by recipient
   * @return the subset description length
   */
  public static double subsetDL(double t, double k, double p){
    double rt = Utils.gr(p, 0.0) ? (- k*Utils.log2(p)) : 0.0;
    rt -= (t-k)*Utils.log2(1-p);
    return rt;
  }
    
    
  /** 
   * The description length of the theory for a given rule.  Computed as:<br>
   *                 0.5* [||k||+ S(t, k, k/t)]<br>
   * where k is the number of antecedents of the rule; t is the total
   * possible antecedents that could appear in a rule; ||K|| is the 
   * universal prior for k , log2*(k) and S(t,k,p) = -k*log2(p)-(n-k)log2(1-p)
   * is the subset encoding length.<p>
   *
   * Details see Quilan: "MDL and categorical theories (Continued)",ML95
   *
   * @param index the index of the given rule (assuming correct)
   * @return the theory DL, weighted if weight != 1.0
   */
  public double theoryDL(int index){
	
    double k = ((Rule)m_Ruleset.elementAt(index)).size();
	
    if(k == 0)
      return 0.0;
	
    double tdl = Utils.log2(k);               	    
    if(k > 1)                           // Approximation
      tdl += 2.0 * Utils.log2(tdl);   // of log2 star	
    tdl += subsetDL(m_Total, k, k/m_Total);
    //System.out.println("!!!theory: "+MDL_THEORY_WEIGHT * REDUNDANCY_FACTOR * tdl);
    return MDL_THEORY_WEIGHT * REDUNDANCY_FACTOR * tdl;
  }
     

  /** 
   * The description length of data given the parameters of the data
   * based on the ruleset. <p>
   * Details see Quinlan: "MDL and categorical theories (Continued)",ML95<p>
   *
   * @param expFPOverErr expected FP/(FP+FN)
   * @param cover coverage
   * @param uncover uncoverage
   * @param fp False Positive
   * @param fn False Negative
   * @return the description length
   */
  public static double dataDL(double expFPOverErr, double cover, 
			      double uncover, double fp, double fn){
    double totalBits = Utils.log2(cover+uncover+1.0); // how many data?
    double coverBits, uncoverBits; // What's the error?
    double expErr;                 // Expected FP or FN

    if(Utils.gr(cover, uncover)){
      expErr = expFPOverErr*(fp+fn);
      coverBits = subsetDL(cover, fp, expErr/cover);
      uncoverBits = Utils.gr(uncover, 0.0) ? 
	subsetDL(uncover, fn, fn/uncover) : 0.0;	    
    }
    else{
      expErr = (1.0-expFPOverErr)*(fp+fn);
      coverBits = Utils.gr(cover, 0.0) ? 
	subsetDL(cover, fp, fp/cover) : 0.0;
      uncoverBits = subsetDL(uncover, fn, expErr/uncover);
    }
	
    /*
      System.err.println("!!!cover: " + cover + "|uncover" + uncover +
      "|coverBits: "+coverBits+"|uncBits: "+ uncoverBits+
      "|FPRate: "+expFPOverErr + "|expErr: "+expErr+
      "|fp: "+fp+"|fn: "+fn+"|total: "+totalBits);
    */
    return (totalBits + coverBits + uncoverBits);
  }

    
  /**
   * Calculate the potential to decrease DL of the ruleset,
   * i.e. the possible DL that could be decreased by deleting
   * the rule whose index and simple statstics are given.  
   * If there's no potentials (i.e. smOrEq 0 && error rate < 0.5),
   * it returns NaN. <p>
   *
   * The way this procedure does is copied from original RIPPER
   * implementation and is quite bizzare because it 
   * does not update the following rules' stats recursively 
   * any more when testing each rule, which means it assumes
   * after deletion no data covered by the following rules (or
   * regards the deleted rule as the last rule).  Reasonable 
   * assumption?<p>
   *
   * @param index the index of the rule in m_Ruleset to be deleted
   * @param expFPOverErr expected FP/(FP+FN)
   * @param rulesetStat the simple statistics of the ruleset, updated
   *                    if the rule should be deleted
   * @param ruleStat the simple statistics of the rule to be deleted
   * @param checkErr whether check if error rate >= 0.5
   * @return the potential DL that could be decreased
   */
  public double potential(int index, double expFPOverErr, 
			  double[] rulesetStat, double[] ruleStat,
			  boolean checkErr){
    //System.out.println("!!!inside potential: ");
    // Restore the stats if deleted
    double pcov = rulesetStat[0] - ruleStat[0];
    double puncov = rulesetStat[1] + ruleStat[0];
    double pfp = rulesetStat[4] - ruleStat[4];
    double pfn = rulesetStat[5] + ruleStat[2];

    double dataDLWith = dataDL(expFPOverErr, rulesetStat[0], 
			       rulesetStat[1], rulesetStat[4], 
			       rulesetStat[5]);
    double theoryDLWith = theoryDL(index);
    double dataDLWithout = dataDL(expFPOverErr, pcov, puncov, pfp, pfn);

    double potential = dataDLWith + theoryDLWith - dataDLWithout;
    double err = ruleStat[4] / ruleStat[0];
    /*System.out.println("!!!"+dataDLWith +" | "+ 
      theoryDLWith + " | " 
      +dataDLWithout+"|"+ruleStat[4] + " / " + ruleStat[0]);
    */
    boolean overErr = Utils.grOrEq(err, 0.5);
    if(!checkErr)
      overErr = false;
	
    if(Utils.grOrEq(potential, 0.0) || overErr){ 
      // If deleted, update ruleset stats.  Other stats do not matter
      rulesetStat[0] = pcov;
      rulesetStat[1] = puncov;
      rulesetStat[4] = pfp;
      rulesetStat[5] = pfn;
      return potential;
    }
    else
      return Double.NaN;
  }
    
    
  /**
   * Compute the minimal data description length of the ruleset
   * if the rule in the given position is deleted.<br>
   * The min_data_DL_if_deleted = data_DL_if_deleted - potential
   *
   * @param index the index of the rule in question
   * @param expFPRate expected FP/(FP+FN), used in dataDL calculation
   * @param checkErr whether check if error rate >= 0.5
   * @return the minDataDL
   */
  public double minDataDLIfDeleted(int index, double expFPRate,
				   boolean checkErr){
    //System.out.println("!!!Enter without: ");
    double[] rulesetStat = new double[6]; // Stats of ruleset if deleted
    int more = m_Ruleset.size() - 1 - index; // How many rules after?
    FastVector indexPlus = new FastVector(more); // Their stats
	
    // 0...(index-1) are OK	
    for(int j=0; j<index; j++){
      // Covered stats are cumulative
      rulesetStat[0] += ((double[])m_SimpleStats.elementAt(j))[0];
      rulesetStat[2] += ((double[])m_SimpleStats.elementAt(j))[2];
      rulesetStat[4] += ((double[])m_SimpleStats.elementAt(j))[4];
    }
	
    // Recount data from index+1
    Instances data = (index == 0) ?  
      m_Data : ((Instances[])m_Filtered.elementAt(index-1))[1];	
    //System.out.println("!!!without: " + data.sumOfWeights());

    for(int j=(index+1); j<m_Ruleset.size(); j++){
      double[] stats = new double[6];
      Instances[] split = computeSimpleStats(j, data, stats, null);
      indexPlus.addElement(stats);
      rulesetStat[0] += stats[0];
      rulesetStat[2] += stats[2];
      rulesetStat[4] += stats[4];	   
      data = split[1];
    }
    // Uncovered stats are those of the last rule
    if(more > 0){
      rulesetStat[1] = ((double[])indexPlus.lastElement())[1];
      rulesetStat[3] = ((double[])indexPlus.lastElement())[3];
      rulesetStat[5] = ((double[])indexPlus.lastElement())[5];
    }
    else if(index > 0){
      rulesetStat[1] = 
	((double[])m_SimpleStats.elementAt(index-1))[1];
      rulesetStat[3] =
	((double[])m_SimpleStats.elementAt(index-1))[3];
      rulesetStat[5] = 
	((double[])m_SimpleStats.elementAt(index-1))[5];
    }	
    else{ // Null coverage
      rulesetStat[1] = ((double[])m_SimpleStats.elementAt(0))[0] +
	((double[])m_SimpleStats.elementAt(0))[1];
      rulesetStat[3] = ((double[])m_SimpleStats.elementAt(0))[3] +
	((double[])m_SimpleStats.elementAt(0))[4];
      rulesetStat[5] = ((double[])m_SimpleStats.elementAt(0))[2] +
	((double[])m_SimpleStats.elementAt(0))[5];	    
    }
	
    // Potential 
    double potential = 0;
    for(int k=index+1; k<m_Ruleset.size(); k++){
      double[] ruleStat = (double[])indexPlus.elementAt(k-index-1);
      double ifDeleted = potential(k, expFPRate, rulesetStat, 
				   ruleStat, checkErr);
      if(!Double.isNaN(ifDeleted))
	potential += ifDeleted;
    }

    // Data DL of the ruleset without the rule
    // Note that ruleset stats has already been updated to reflect 
    // deletion if any potential		
    double dataDLWithout = dataDL(expFPRate, rulesetStat[0], 
				  rulesetStat[1], rulesetStat[4], 
				  rulesetStat[5]);
    //System.out.println("!!!without: "+dataDLWithout + " |potential: "+
    //		   potential);
    // Why subtract potential again?  To reflect change of theory DL??
    return (dataDLWithout - potential);
  }    
    
    
  /**
   * Compute the minimal data description length of the ruleset
   * if the rule in the given position is NOT deleted.<br>
   * The min_data_DL_if_n_deleted = data_DL_if_n_deleted - potential
   *
   * @param index the index of the rule in question
   * @param expFPRate expected FP/(FP+FN), used in dataDL calculation
   * @param checkErr whether check if error rate >= 0.5
   * @return the minDataDL
   */
  public double minDataDLIfExists(int index, double expFPRate,
				  boolean checkErr){
    //	System.out.println("!!!Enter with: ");
    double[] rulesetStat = new double[6]; // Stats of ruleset if rule exists
    for(int j=0; j<m_SimpleStats.size(); j++){
      // Covered stats are cumulative
      rulesetStat[0] += ((double[])m_SimpleStats.elementAt(j))[0];
      rulesetStat[2] += ((double[])m_SimpleStats.elementAt(j))[2];
      rulesetStat[4] += ((double[])m_SimpleStats.elementAt(j))[4];
      if(j == m_SimpleStats.size()-1){ // Last rule
	rulesetStat[1] = ((double[])m_SimpleStats.elementAt(j))[1];
	rulesetStat[3] = ((double[])m_SimpleStats.elementAt(j))[3];
	rulesetStat[5] = ((double[])m_SimpleStats.elementAt(j))[5];
      }	    
    }
	
    // Potential 
    double potential = 0;
    for(int k=index+1; k<m_SimpleStats.size(); k++){
      double[] ruleStat = (double[])getSimpleStats(k);
      double ifDeleted = potential(k, expFPRate, rulesetStat, 
				   ruleStat, checkErr);
      if(!Double.isNaN(ifDeleted))
	potential += ifDeleted;
    }
	
    // Data DL of the ruleset without the rule
    // Note that ruleset stats has already been updated to reflect deletion
    // if any potential	
    double dataDLWith = dataDL(expFPRate, rulesetStat[0], 
			       rulesetStat[1], rulesetStat[4], 
			       rulesetStat[5]);	
    //System.out.println("!!!with: "+dataDLWith + " |potential: "+
    //		   potential);
    return (dataDLWith - potential);
  }
    
    
  /**
   * The description length (DL) of the ruleset relative to if the
   * rule in the given position is deleted, which is obtained by: <br>
   * MDL if the rule exists - MDL if the rule does not exist <br>
   * Note the minimal possible DL of the ruleset is calculated(i.e. some
   * other rules may also be deleted) instead of the DL of the current
   * ruleset.<p>
   *
   * @param index the given position of the rule in question 
   *              (assuming correct)
   * @param expFPRate expected FP/(FP+FN), used in dataDL calculation
   * @param checkErr whether check if error rate >= 0.5
   * @return the relative DL
   */
  public double relativeDL(int index, double expFPRate, boolean checkErr){ 
		 
    return (minDataDLIfExists(index, expFPRate, checkErr) 
	    + theoryDL(index) - 
	    minDataDLIfDeleted(index, expFPRate, checkErr));
  }  
    
    
  /**
   * Try to reduce the DL of the ruleset by testing removing the rules
   * one by one in reverse order and update all the stats
   * @param expFPRate expected FP/(FP+FN), used in dataDL calculation
   * @param checkErr whether check if error rate >= 0.5
   */
  public void reduceDL(double expFPRate, boolean checkErr){
	
    boolean needUpdate = false;
    double[] rulesetStat = new double[6];
    for(int j=0; j<m_SimpleStats.size(); j++){
      // Covered stats are cumulative
      rulesetStat[0] += ((double[])m_SimpleStats.elementAt(j))[0];
      rulesetStat[2] += ((double[])m_SimpleStats.elementAt(j))[2];
      rulesetStat[4] += ((double[])m_SimpleStats.elementAt(j))[4];
      if(j == m_SimpleStats.size()-1){ // Last rule
	rulesetStat[1] = ((double[])m_SimpleStats.elementAt(j))[1];
	rulesetStat[3] = ((double[])m_SimpleStats.elementAt(j))[3];
	rulesetStat[5] = ((double[])m_SimpleStats.elementAt(j))[5];
      }	    
    }
	
    // Potential 
    for(int k=m_SimpleStats.size()-1; k>=0; k--){
	    	
      double[] ruleStat = (double[])m_SimpleStats.elementAt(k);

      // rulesetStat updated
      double ifDeleted = potential(k, expFPRate, rulesetStat, 
				   ruleStat, checkErr);
      if(!Double.isNaN(ifDeleted)){  
	/*System.err.println("!!!deleted ("+k+"): save "+ifDeleted
	  +" | "+rulesetStat[0]
	  +" | "+rulesetStat[1]
	  +" | "+rulesetStat[4]
	  +" | "+rulesetStat[5]);
	*/
	
	if(k == (m_SimpleStats.size()-1))
	    removeLast();
	else{
	    m_Ruleset.removeElementAt(k);
	    needUpdate = true;
	}
      }
    }
	
    if(needUpdate){
      m_Filtered = null;
      m_SimpleStats = null;
      countData();
    }
  }
    
  /**
   * Remove the last rule in the ruleset as well as it's stats.
   * It might be useful when the last rule was added for testing
   * purpose and then the test failed
   */
  public void removeLast(){
    int last = m_Ruleset.size()-1;
    m_Ruleset.removeElementAt(last);
    m_Filtered.removeElementAt(last);
    m_SimpleStats.removeElementAt(last);	
    if(m_Distributions != null)
	m_Distributions.removeElementAt(last);
  }

  /**
   * Static utility function to count the data covered by the 
   * rules after the given index in the given rules, and then
   * remove them.  It returns the data not covered by the
   * successive rules.
   *
   * @param data the data to be processed
   * @param rules the ruleset
   * @param index the given index
   * @return the data after processing
   */
  public static Instances rmCoveredBySuccessives(Instances data, FastVector rules, int index){
    Instances rt = new Instances(data, 0);

    for(int i=0; i < data.numInstances(); i++){
      Instance datum = data.instance(i);
      boolean covered = false;	    
	    
      for(int j=index+1; j<rules.size();j++){
	Rule rule = (Rule)rules.elementAt(j);
	if(rule.covers(datum)){
	  covered = true;
	  break;
	}
      }

      if(!covered)
	rt.add(datum);
    }	
    return rt;
  } 
    
  /** 
   * Stratify the given data into the given number of bags based on the class
   * values.  It differs from the <code>Instances.stratify(int fold)</code>
   * that before stratification it sorts the instances according to the 
   * class order in the header file.  It assumes no missing values in the class.
   * 
   * @param data the given data
   * @param folds the given number of folds
   * @param rand the random object used to randomize the instances
   * @return the stratified instances
   */
  public static final Instances stratify(Instances data, int folds, Random rand){
    if(!data.classAttribute().isNominal())
      return data;
	
    Instances result = new Instances(data, 0);
    Instances[] bagsByClasses = new Instances[data.numClasses()];
	
    for(int i=0; i < bagsByClasses.length; i++)
      bagsByClasses[i] = new Instances(data, 0);
	
    // Sort by class	
    for(int j=0; j < data.numInstances(); j++){
      Instance datum = data.instance(j);
      bagsByClasses[(int)datum.classValue()].add(datum);
    }
	
    // Randomize each class
    for(int j=0; j < bagsByClasses.length; j++)
      bagsByClasses[j].randomize(rand);
	
    for(int k=0; k < folds; k++){
      int offset = k, bag = 0;
    oneFold:
      while (true){	
	while(offset >= bagsByClasses[bag].numInstances()){
	  offset -= bagsByClasses[bag].numInstances();
	  if (++bag >= bagsByClasses.length)// Next bag
	    break oneFold;      	   
	}	
	    
	result.add(bagsByClasses[bag].instance(offset));
	offset += folds;				
      }
    }
	
    return result;
  }

  /** 
   * Compute the combined DL of the ruleset in this class, i.e. theory 
   * DL and data DL.  Note this procedure computes the combined DL
   * according to the current status of the ruleset in this class
   * 
   * @param expFPRate expected FP/(FP+FN), used in dataDL calculation
   * @param predicted the default classification if ruleset covers null
   * @return the combined class
   */
  public double combinedDL(double expFPRate, double predicted){
    double rt = 0;
    
    if(getRulesetSize() > 0) {
      double[] stats = (double[])m_SimpleStats.lastElement();
      for(int j=getRulesetSize()-2; j >= 0; j--){
	stats[0] += getSimpleStats(j)[0];
	stats[2] += getSimpleStats(j)[2];
	stats[4] += getSimpleStats(j)[4];
      }
      rt += dataDL(expFPRate, stats[0], stats[1], 
		   stats[4], stats[5]);     // Data DL      
    }
    else{ // Null coverage ruleset
      double fn = 0.0;
      for(int j=0; j < m_Data.numInstances(); j++)
	if((int)m_Data.instance(j).classValue() == (int)predicted)
	  fn += m_Data.instance(j).weight();
      rt += dataDL(expFPRate, 0.0, m_Data.sumOfWeights(), 0.0, fn);	
    }     
    
    for(int i=0; i<getRulesetSize(); i++) // Theory DL
      rt += theoryDL(i);     
    
    return rt;
  }
  
  /** 
   * Patition the data into 2, first of which has (numFolds-1)/numFolds of
   * the data and the second has 1/numFolds of the data
   *
   * 
   * @param data the given data
   * @param numFolds the given number of folds
   * @return the patitioned instances
   */
  public static final Instances[] partition(Instances data, int numFolds){
    Instances[] rt = new Instances[2];
    int splits = data.numInstances() * (numFolds - 1) / numFolds;
    
    rt[0] = new Instances(data, 0, splits);
    rt[1] = new Instances(data, splits, data.numInstances()-splits);
    
    return rt;
  }  
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 8034 $");
  }
}
